glassopath {glasso} | R Documentation |
Estimates a sparse inverse covariance matrix using a lasso (L1) penalty, along a path of values for the regularization parameter
glassopath(s, rholist=NULL, thr=1.0e-4, maxit=1e4, approx=FALSE, penalize.diagonal=TRUE, w.init=NULL,wi.init=NULL, trace=FALSE)
s |
Covariance matrix:p by p matrix (symmetric) |
rholist |
Vector of non-negative regularization parameters for the lasso. Should be increasing from smallest to largest; actual path is computed from largest to smallest value of rho). If NULL, 10 values in a (hopefully reasonable) range are used. Note that the same parameter rholist[j] is used for all entries of the inverse covariance matrix; different penalties for different entries are not allowed. |
thr |
Threshold for convergence. Default value is 1e-4. Iterations stop when average absolute parameter change is less than thr * ave(abs(offdiag(s))) |
maxit |
Maximum number of iterations of outer loop. Default 10,000 |
approx |
Approximation flag: if true, computes Meinhausen-Buhlmann(2006) approximation |
penalize.diagonal |
Should diagonal of inverse covariance be penalized? Dafault TRUE. |
w.init |
Optional starting values for estimated covariance matrix (p by p). Only needed when start="warm" is specified |
wi.init |
Optional starting values for estimated inverse covariance matrix (p by p) Only needed when start="warm" is specified |
trace |
Flag for printing out information as iterations proceed. Default FALSE |
Estimates a sparse inverse covariance matrix using a lasso (L1) penalty, along a path of regularization paramaters, using the approach of Friedman, Hastie and Tibshirani (2007). The Meinhausen-Buhlmann (2006) approximation is also implemented. The algorithm can also be used to estimate a graph with missing edges, by specifying which edges to omit in the zero argument, and setting rho=0. Or both fixed zeroes for some elements and regularization on the other elements can be specified.
A list with components
w |
Estimated covariance matrices, an array of dimension (nrow(s),ncol(n), length(rholist)) |
wi |
Estimated inverse covariance matrix, an array of dimension (nrow(s),ncol(n), length(rholist)) |
approx |
Value of input argument approx |
rholist |
Values of regularization parameter used |
errflag |
values of error flag (0 means no memory allocation error) |
Jerome Friedman, Trevor Hastie and Robert Tibshirani (2007). Sparse inverse covariance estimation with the lasso. Biostatistics 2007. http://www-stat.stanford.edu/~tibs/ftp/graph.pdf
Meinshausen, N. and Buhlmann, P.(2006) High dimensional graphs and variable selection with the lasso. Annals of Statistics,34, p1436-1462.
set.seed(100) x<-matrix(rnorm(50*20),ncol=20) s<- var(x) a<-glassopath(s)